Dr.-Ing. Jörg Brummund

2025

• Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functionsKalina, K. A., Brummund, J., Sun, W. & Kästner, M., 21 Jan 2025, In: Computer Methods in Applied Mechanics and Engineering. 437, 117725Electronic (full-text) versionResearch output: Contribution to journal > Research article

2024

• Viscoelasticty with physics-augmented neural networks: model formulation and training methods without prescribed internal variablesRosenkranz, M., Kalina, K. A., Brummund, J., Sun, W. & Kästner, M., 6 May 2024, In: Computational Mechanics : solids, fluids, engineered materials, aging infrastructure, molecular dynamics, heat transfer, manufacturing processes, optimization, fracture & integrity. 74, 6, p. 1279-1301, 23 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article
• Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteriaKalina, K. A., Gebhart, P., Brummund, J., Linden, L., Sun, W. & Kästner, M., Mar 2024, In: Computer Methods in Applied Mechanics and Engineering. 421, 116739Electronic (full-text) versionResearch output: Contribution to journal > Research article

2023

• Neural networks meet hyperelasticity: A guide to enforcing physicsLinden, L., Klein, D., Kalina, K. A., Brummund, J., Weeger, O. & Kästner, M., Oct 2023, In: Journal of the Mechanics and Physics of Solids. 179, 105363Electronic (full-text) versionResearch output: Contribution to journal > Research article
• Overview of phase-field models for fatigue fracture in a unified frameworkKalina, M., Schneider, T., Brummund, J. & Kästner, M., 4 Aug 2023, In: Engineering Fracture Mechanics. 288, 109318Electronic (full-text) versionResearch output: Contribution to journal > Research article
• A comparative study on different neural network architectures to model inelasticityRosenkranz, M., Kalina, K. A., Brummund, J. & Kästner, M., 18 Jul 2023, In: International Journal for Numerical Methods in Engineering. 124 (2023), 21, p. 4802-4840, 39 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article
• FEANN: an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data miningKalina, K. A., Linden, L., Brummund, J. & Kästner, M., 8 Feb 2023, In: Computational Mechanics : solids, fluids, engineered materials, aging infrastructure, molecular dynamics, heat transfer, manufacturing processes, optimization, fracture & integrity. 71, 5, p. 827-851, 25 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article

2022

• Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networksKalina, K. A., Linden, L., Brummund, J., Metsch, P. & Kästner, M., Jan 2022, In: Computational Mechanics : solids, fluids, engineered materials, aging infrastructure, molecular dynamics, heat transfer, manufacturing processes, optimization, fracture & integrity. 69, p. 213-232, 20 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article

2021

• Jump conditions in phase‐field modeling of interface fractureHansen-Dörr, A. C., Brummund, J. & Kästner, M., Jan 2021Electronic (full-text) versionResearch output: Contribution to conferences > Paper
• A macroscopic model for magneto‐active elastomers based on microscopic simulationsKalina, K. A., Metsch, P., Brummund, J. & Kästner, M., 2021, In: Proceedings in Applied Mathematics and Mechanics: PAMM. 20 (2020), 1, p. e202000208, 2 p.Electronic (full-text) versionResearch output: Contribution to journal > Conference article